Numerous machine learning models connected to healthcare are currently in use, and most of them concentrate on identifying distinct conditions. Our study has developed a system that employs a single-user interface to forecast several diseases. Numerous illnesses, like diabetes, heart disease, chronic renal disease, and cancer, can be predicted by the suggested model. Humanity is at risk from these diseases if treatment is not received. Consequently, early identification and detection of these conditions can save many lives. To forecast diseases, this study aims to apply several classification techniques, including Gaussian naive Bayes, SVM, K-Nearest Neighbor, Decision Tree, and Logistic Regression. Moreover, to attain the highest level of accuracy in the anticipated findings, multiple datasets are employed (one dataset for each disease). The primary goal is to develop a web-based system that can use ML to predict numerous diseases, like diabetes, cancer, heart problems, and chronic kidney problems.
Blood sugar disorders, Heart conditions, Chronic renal disease, Cancer, KNN, SVM, Single user interface, Decision Tree, Random Forest, Logistic Regression
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